Feature propagation on image webs for enhanced image retrieval. Brachmann, E.; Spehr, M.; and Gumhold, S. In
abstract   bibtex   
The bag-of-features model is often deployed in content-based image retrieval to measure image similarity. In cases where the visual appearance of semantically similar images differs largely, feature histograms mismatch and the model fails. We increase the robustness of feature histograms by automatically augmenting them with features of related images. We establish image relations by image web construction and adapt a label propagation scheme from the domain of semi-supervised learning for feature augmentation. While the benefit of feature augmentation has been shown before, our approach refrains from the use of semantic labels. Instead we show how to increase the performance of the bag-of-features model substantially on a completely unlabeled image corpus.
@inproceedings{ Brachmann-2013-FPI,
  author = {Eric Brachmann and
               Marcel Spehr and
               Stefan Gumhold},
  title = {Feature propagation on image webs for enhanced image retrieval},
  abstract = {
The bag-of-features model is often deployed in content-based image retrieval to measure image similarity. 
In cases where the visual appearance of semantically similar images differs largely, feature histograms 
mismatch and the model fails. We increase the robustness of feature histograms by automatically augmenting
them with features of related images. We establish image relations by image web construction and adapt a 
label propagation scheme from the domain of semi-supervised learning for feature augmentation. While the 
benefit of feature augmentation has been shown before, our approach refrains from the use of semantic 
labels. Instead we show how to increase the performance of the bag-of-features model substantially on a
completely unlabeled image corpus.
}
}
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